The application of object detection systems is increasingly relevant across a wide variety of technology domains, including, for example, transport, surveillance, medicine and security. Among these, a particular application of such systems involves aiding the navigation of a vehicle by a user, or, increasingly, in aiding in automating vehicular navigation entirely. In order to be effective, however, automatic vehicle navigation systems must maintain a constant awareness of the surroundings of the vehicle. This includes the timely detection of on-road obstacles, lane markings, and any writing on the road surface.
Existing vehicular navigation systems may use sensors to detect obstacles. However, the usage of sensors is expensive and requires a relatively high degree of maintenance. An additional drawback of sensor based approaches is that such systems cannot generally distinguish between types of obstacles. For example, sensor based systems may be unable to distinguish between a car and a pedestrian, labeling both as generic obstacles in the path of the vehicle. In another approach, number plates may be used to detect cars on the road, but such a technique may necessitate that the object to be detected is in relatively close proximity. Additionally, number plate recognition may require template matching by the navigation system, i.e., matching the detected number plate to a set of known templates. Template matching requires a large amount of data handling, however, and is therefore a serious limitation to real time computation.
In another approach, the geographic features of the road are used for lane detection, but scaling such an approach to the detection of multiple artificial lane markers, and, thusly, multiple lanes is computationally complex and hence problematic.
A successful automatic vehicle navigation system then, at its core, should address different types of detection problems, which may necessitate an integrated approach. Accordingly, there is a need for multi-dimensional object detection that is able to provide timely lane and obstacle detection, recognize existing road signage and provide warnings or alerts to a user when impediments are detected.